IMEx Databases: Displaying Molecular Interactions into a Single, Standards-Compliant Dataset

Utilizing systems genetics to enhance understanding into molecular targets of skin cancer

Despite progress made with immune checkpoint inhibitors and targeted therapies, skin cancer remains a significant public health concern in the United States. The intricacies of the disease, encompassing genetics, immune responses, and external factors, call for a comprehensive approach. Techniques in systems genetics, including transcriptional correlation analysis, functional pathway enrichment analysis, and protein-protein interaction network analysis, prove valuable in deciphering intricate molecular mechanisms and identifying potential diagnostic and therapeutic targets for skin cancer. Recent studies demonstrate the efficacy of these techniques in uncovering molecular processes and pinpointing diagnostic markers for various skin cancer types, highlighting the potential of systems genetics in advancing innovative therapies. While certain limitations exist, such as generalizability and contextualization of external factors, the ongoing progress in AI technologies provides hope in overcoming these challenges. By providing protocols and a practical example involving Braf, we aim to inspire early-career experimental dermatologists to adopt these tools and seamlessly integrate these techniques into their skin cancer research, positioning them at the forefront of innovative approaches in combating this devastating disease.

The alliance of genome resources: transforming comparative genomics

Comparing genomic and biological characteristics across multiple species is essential to using model systems to investigate the molecular and cellular mechanisms underlying human biology and disease and to translate mechanistic insights from studies in model organisms for clinical applications. Building a scalable knowledge commons platform that supports cross-species comparison of rich, expertly curated knowledge regarding gene function, phenotype, and disease associations available for model organisms and humans is the primary mission of the Alliance of Genome Resources (the Alliance). The Alliance is a consortium of seven model organism knowledgebases (mouse, rat, yeast, nematode, zebrafish, frog, fruit fly) and the Gene Ontology resource. The Alliance uses a common set of gene ortholog assertions as the basis for comparing biological annotations across the organisms represented in the Alliance. The major types of knowledge associated with genes that are represented in the Alliance database currently include gene function, phenotypic alleles and variants, human disease associations, pathways, gene expression, and both protein-protein and genetic interactions. The Alliance has enhanced the ability of researchers to easily compare biological annotations for common data types across model organisms and human through the implementation of shared programmatic access mechanisms, data-specific web pages with a unified "look and feel", and interactive user interfaces specifically designed to support comparative biology. The modular infrastructure developed by the Alliance allows the resource to serve as an extensible "knowledge commons" capable of expanding to accommodate additional model organisms.

Multi-omics analysis of MRPL-13 as a tumor-promoting marker from pan-cancer to lung adenocarcinoma

BACKGROUND

As a member of the mitochondrial ribosomal protein family, mitochondrial ribosomal protein L13 (MRPL13) is responsible for synthesizing mitochondrial proteins in cells. Several studies have indicated that MRPL13 is associated with the proliferation cycle, migration ability, apoptosis and autophagy of cancer cells. However, a thorough examination of MRPL13 across cancers remains uncertain. Therefore, we tried to clarify the relationship between MRPL13 and pan-cancer, and verified it in lung adenocarcinoma by various methods. Finally, our research is expected to reveal new targets for pan-cancer treatment and improve the prognosis of cancer patients.

METHODS

Using bioinformatics tools, we quantified the differential expression of MRPL13 between cancer tissues and corresponding or noncorresponding normal tissues across cancers. We also analyzed the relationships between MRPL13 expression levels and several factors, including diagnosis, prognosis, mutation, functional signaling pathways, immune infiltration, RNA modification, and the relationship with cuproptosis-related genes. Furthermore, we studied the relationship between the expression level of MRPL13 across cancers and the change in cancer functional status through single-cell data. In addition, quantitative experiments (PCR and Western blot) proved that the expression of MRPL13 was significantly different between LUAD and control samples. Finally, the effect of knocking out MRPL13 on cancer cells was compared by gene silencing experiments. In summary, we used a combination of bioinformatics and experimental applications to study the potential roles of MRPL13 in cancer.

RESULTS

After conducting a multidimensional analysis, we found that the application of MRPL13 multigroup analysis can effectively improve the diagnostic efficiency of various cancers and predict the prognosis of cancer. Moreover, MRPL13 in pan-cancer is related to the cancer immune infiltration pattern, methylation level and cuproptosis-related genes. Furthermore, single-cell data analysis showed that the modules of metastasis, EMT, cell cycle, DNA repair, invasion, DNA damage and proliferation were positively correlated with the expression of MRPL13 in LUAD (Lung adenocarcinoma), while the modules of hypoxia and inflammation were negatively correlated. Moreover, through quantitative experiments, we observed higher expression of MRPL13 in cancer tissues at the RNA or protein level. Knockdown of MRPL13 in LUAD led to decreased cancer cell survival, delayed tumor division and migration, reduced invasion, and increased cancer cell apoptosis.

CONCLUSIONS

Our study demonstrates the potential of using MRPL13 as a molecular biomarker for diagnosing and suggesting the prognosis of certain malignant tumors. Furthermore, our research shows that MRPL13 may be an effective therapeutic target for lung adenocarcinoma.

Building, Visualizing, and Analyzing Glycosaminoglycan-Protein Interaction Networks

This chapter describes how to generate, visualize, and analyze interaction networks of glycosaminoglycans (GAGs), which are linear polyanionic polysaccharides mostly located at the cell surface and in the extracellular matrix. The protocol is divided into three major steps: (1) the collection of GAG-mediated interaction data, (2) the visualization of GAG interaction networks, and (3) the computational enrichment analyses of these networks to identify their overrepresented features (e.g., protein domains, location, molecular functions, and biological pathways) compared to a reference proteome. These analyses are critical to interpret GAG interactomic datasets, decipher their specificities and functions, and ultimately identify GAG-protein interactions to target for therapeutic purpose.